from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
     import torch

if is_tf_available():
     import tensorflow as tf
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""image_processor"""]
   __lowerCAmelCase           =				"""SamImageProcessor"""







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__(lowerCamelCase_				)
    UpperCamelCase			  =      self.image_processor
    UpperCamelCase			  =      -10
    UpperCamelCase			  =      self.image_processor.size["""longest_edge"""]







   def __call__(     self						:						Any   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   **lowerCamelCase_						:						Union[str, Any]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processor(
        lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   **lowerCamelCase_   ,   )

    # pop arguments that are not used in the foward but used nevertheless
    UpperCamelCase			  =      encoding_image_processor["""original_sizes"""]

    if hasattr(lowerCamelCase_   ,   """numpy"""				):  # Checks if Torch or TF tensor
     UpperCamelCase			  =      original_sizes.numpy()

    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._check_and_preprocess_points(
        input_points=lowerCamelCase_   ,   input_labels=lowerCamelCase_   ,   input_boxes=lowerCamelCase_   ,   )

    UpperCamelCase			  =      self._normalize_and_convert(
        lowerCamelCase_   ,   lowerCamelCase_   ,   input_points=lowerCamelCase_   ,   input_labels=lowerCamelCase_   ,   input_boxes=lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   )

    return encoding_image_processor







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Any="pt"   ,   ):





    """simple docstring"""


    if input_points is not None:
     if len(lowerCamelCase_				) != len(lowerCamelCase_				):
      UpperCamelCase			  =      [
          self._normalize_coordinates(self.target_size   ,   lowerCamelCase_   ,   original_sizes[0]				) for point in input_points
      ]
     else:
      UpperCamelCase			  =      [
          self._normalize_coordinates(self.target_size   ,   lowerCamelCase_   ,   lowerCamelCase_				)
          for point, original_size in zip(lowerCamelCase_   ,   lowerCamelCase_				)
      ]
     # check that all arrays have the same shape
     if not all(point.shape == input_points[0].shape for point in input_points				):
      if input_labels is not None:
       UpperCamelCase  ,     UpperCamelCase			  =      self._pad_points_and_labels(lowerCamelCase_   ,   lowerCamelCase_				)

     UpperCamelCase			  =      np.array(lowerCamelCase_				)

    if input_labels is not None:
     UpperCamelCase			  =      np.array(lowerCamelCase_				)

    if input_boxes is not None:
     if len(lowerCamelCase_				) != len(lowerCamelCase_				):
      UpperCamelCase			  =      [
          self._normalize_coordinates(self.target_size   ,   lowerCamelCase_   ,   original_sizes[0]   ,   is_bounding_box=lowerCamelCase_				)
          for box in input_boxes
      ]
     else:
      UpperCamelCase			  =      [
          self._normalize_coordinates(self.target_size   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   is_bounding_box=lowerCamelCase_				)
          for box, original_size in zip(lowerCamelCase_   ,   lowerCamelCase_				)
      ]
     UpperCamelCase			  =      np.array(lowerCamelCase_				)

    if input_boxes is not None:
     if return_tensors == "pt":
      UpperCamelCase			  =      torch.from_numpy(lowerCamelCase_				)
      # boxes batch size of 1 by default
      UpperCamelCase			  =      input_boxes.unsqueeze(1				) if len(input_boxes.shape				) != 3 else input_boxes
     elif return_tensors == "tf":
      UpperCamelCase			  =      tf.convert_to_tensor(lowerCamelCase_				)
      # boxes batch size of 1 by default
      UpperCamelCase			  =      tf.expand_dims(lowerCamelCase_   ,   1				) if len(input_boxes.shape				) != 3 else input_boxes
     encoding_image_processor.update({"""input_boxes""": input_boxes}				)
    if input_points is not None:
     if return_tensors == "pt":
      UpperCamelCase			  =      torch.from_numpy(lowerCamelCase_				)
      # point batch size of 1 by default
      UpperCamelCase			  =      input_points.unsqueeze(1				) if len(input_points.shape				) != 4 else input_points
     elif return_tensors == "tf":
      UpperCamelCase			  =      tf.convert_to_tensor(lowerCamelCase_				)
      # point batch size of 1 by default
      UpperCamelCase			  =      tf.expand_dims(lowerCamelCase_   ,   1				) if len(input_points.shape				) != 4 else input_points
     encoding_image_processor.update({"""input_points""": input_points}				)
    if input_labels is not None:
     if return_tensors == "pt":
      UpperCamelCase			  =      torch.from_numpy(lowerCamelCase_				)
      # point batch size of 1 by default
      UpperCamelCase			  =      input_labels.unsqueeze(1				) if len(input_labels.shape				) != 3 else input_labels
     elif return_tensors == "tf":
      UpperCamelCase			  =      tf.convert_to_tensor(lowerCamelCase_				)
      # point batch size of 1 by default
      UpperCamelCase			  =      tf.expand_dims(lowerCamelCase_   ,   1				) if len(input_labels.shape				) != 3 else input_labels
     encoding_image_processor.update({"""input_labels""": input_labels}				)

    return encoding_image_processor







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      max([point.shape[0] for point in input_points]				)
    UpperCamelCase			  =      []
    for i, point in enumerate(lowerCamelCase_				):
     if point.shape[0] != expected_nb_points:
      UpperCamelCase			  =      np.concatenate(
          [point, np.zeros((expected_nb_points - point.shape[0], 2)				) + self.point_pad_value]   ,   axis=0				)
      UpperCamelCase			  =      np.append(input_labels[i]   ,   [self.point_pad_value]				)
     processed_input_points.append(lowerCamelCase_				)
    UpperCamelCase			  =      processed_input_points
    return input_points, input_labels







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						np.ndarray   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						int=False				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      original_size
    UpperCamelCase  ,     UpperCamelCase			  =      self.image_processor._get_preprocess_shape(lowerCamelCase_   ,   longest_edge=lowerCamelCase_				)
    UpperCamelCase			  =      deepcopy(lowerCamelCase_				).astype(lowerCamelCase_				)

    if is_bounding_box:
     UpperCamelCase			  =      coords.reshape(-1   ,   2   ,   2				)

    UpperCamelCase			  =      coords[..., 0] * (new_w / old_w)
    UpperCamelCase			  =      coords[..., 1] * (new_h / old_h)

    if is_bounding_box:
     UpperCamelCase			  =      coords.reshape(-1   ,   4				)

    return coords







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str=None   ,   lowerCamelCase_						:						str=None   ,   lowerCamelCase_						:						Any=None   ,   ):





    """simple docstring"""


    if input_points is not None:
     if hasattr(lowerCamelCase_   ,   """numpy"""				):  # Checks for TF or Torch tensor
      UpperCamelCase			  =      input_points.numpy().tolist()

     if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				) or not isinstance(input_points[0]   ,   lowerCamelCase_				):
      raise ValueError("""Input points must be a list of list of floating points."""				)
     UpperCamelCase			  =      [np.array(lowerCamelCase_				) for input_point in input_points]
    else:
     UpperCamelCase			  =      None

    if input_labels is not None:
     if hasattr(lowerCamelCase_   ,   """numpy"""				):
      UpperCamelCase			  =      input_labels.numpy().tolist()

     if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				) or not isinstance(input_labels[0]   ,   lowerCamelCase_				):
      raise ValueError("""Input labels must be a list of list integers."""				)
     UpperCamelCase			  =      [np.array(lowerCamelCase_				) for label in input_labels]
    else:
     UpperCamelCase			  =      None

    if input_boxes is not None:
     if hasattr(lowerCamelCase_   ,   """numpy"""				):
      UpperCamelCase			  =      input_boxes.numpy().tolist()

     if (
         not isinstance(lowerCamelCase_   ,   lowerCamelCase_				)
         or not isinstance(input_boxes[0]   ,   lowerCamelCase_				)
         or not isinstance(input_boxes[0][0]   ,   lowerCamelCase_				)
     ):
      raise ValueError("""Input boxes must be a list of list of list of floating points."""				)
     UpperCamelCase			  =      [np.array(lowerCamelCase_				).astype(np.floataa				) for box in input_boxes]
    else:
     UpperCamelCase			  =      None

    return input_points, input_labels, input_boxes







   @property
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processor.model_input_names
    return list(dict.fromkeys(lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   *lowerCamelCase_						:						Any   ,   **lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    return self.image_processor.post_process_masks(*lowerCamelCase_   ,   **lowerCamelCase_				)

_SCREAMING_SNAKE_CASE 		=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def 			lowercase(	)		->	None:







 '''simple docstring'''
 UpperCamelCase			  =      input("""Enter message: """       )
 UpperCamelCase			  =      input("""Enter key [alphanumeric]: """       )
 UpperCamelCase			  =      input("""Encrypt/Decrypt [e/d]: """       )

 if mode.lower().startswith("""e"""       ):
  UpperCamelCase			  =      """encrypt"""
  UpperCamelCase			  =      encrypt_message(UpperCamelCase_							,					UpperCamelCase_       )
 elif mode.lower().startswith("""d"""       ):
  UpperCamelCase			  =      """decrypt"""
  UpperCamelCase			  =      decrypt_message(UpperCamelCase_							,					UpperCamelCase_       )

 print(f"""\n{mode.title()}ed message:"""       )
 print(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""encrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""decrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      0
 UpperCamelCase			  =      key.upper()

 for symbol in message:
  UpperCamelCase			  =      LETTERS.find(symbol.upper()       )
  if num != -1:
   if mode == "encrypt":
    num += LETTERS.find(key[key_index]       )
   elif mode == "decrypt":
    num -= LETTERS.find(key[key_index]       )

   num %= len(UpperCamelCase_       )

   if symbol.isupper():
    translated.append(LETTERS[num]       )
   elif symbol.islower():
    translated.append(LETTERS[num].lower()       )

   key_index += 1
   if key_index == len(UpperCamelCase_       ):
    UpperCamelCase			  =      0
  else:
   translated.append(UpperCamelCase_       )
 return "".join(UpperCamelCase_       )


if __name__ == "__main__":
     main()
